Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Network
This addresses network capacity optimization for cellular operators, but it is incremental as it builds on existing methods.
The paper tackled traffic steering in heterogeneous networks by proposing a reinforcement learning algorithm combined with a neural network, which improved efficiency in serving more users with limited resources compared to reference algorithms.
Heterogeneous radio access networks require efficient traffic steering methods to reach near-optimal results in order to maximize network capacity. This paper aims to propose a novel traffic steering algorithm for usage in HetNets, which utilizes a reinforcement learning algorithm in combination with an artificial neural network to maximize total user satisfaction in the simulated cellular network. The novel algorithm was compared with two reference algorithms using network simulation results. The results prove that the novel algorithm provides noticeably better efficiency in comparison with reference algorithms, especially in terms of the number of served users with limited frequency resources of the radio access network.